{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-26T11:05:01.676547",
     "start_time": "2017-06-26T11:04:55.016581"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import os\n",
    "os.chdir('..')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import trading.start\n",
    "import trading.portfolio as portfolio\n",
    "import config.settings\n",
    "from time import sleep\n",
    "from core.utility import *\n",
    "from trading.accountcurve import *\n",
    "import data.db_mongo as db\n",
    "import config.portfolios\n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15, 10\n",
    "p = portfolio.Portfolio(instruments=config.portfolios.p_trade)\n",
    "i = p.instruments\n",
    "from trading.bootstrap import bootstrap\n",
    "from IPython.display import FileLink, FileLinks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to test and integrate new rules\n",
    "\n",
    "When we create new rules, we want to know three basic things:\n",
    "\n",
    "* Does it makes money? - **Sharpe ratio**\n",
    "* How similar is it to existing rules we have in our system? - **Correlation**\n",
    "* How should we weight the forecasts of those rules in our system? - **Boostrapping**\n",
    "\n",
    "### Does it make any money?\n",
    "\n",
    "We test a rule by looking at the Sharpe ratio. Let's take EWMAC and Corn as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-26T11:05:04.659344",
     "start_time": "2017-06-26T11:05:01.678838"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ewmac8</th>\n",
       "      <th>ewmac16</th>\n",
       "      <th>ewmac32</th>\n",
       "      <th>ewmac64</th>\n",
       "      <th>carry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-02-16</th>\n",
       "      <td>2.738041</td>\n",
       "      <td>2.023372</td>\n",
       "      <td>-2.656252</td>\n",
       "      <td>-10.260607</td>\n",
       "      <td>-11.613287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-20</th>\n",
       "      <td>2.602314</td>\n",
       "      <td>2.078657</td>\n",
       "      <td>-2.483146</td>\n",
       "      <td>-10.103146</td>\n",
       "      <td>-11.471541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-21</th>\n",
       "      <td>2.515616</td>\n",
       "      <td>2.143955</td>\n",
       "      <td>-2.307459</td>\n",
       "      <td>-9.942540</td>\n",
       "      <td>-11.328670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-22</th>\n",
       "      <td>2.489667</td>\n",
       "      <td>2.230353</td>\n",
       "      <td>-2.122919</td>\n",
       "      <td>-9.775568</td>\n",
       "      <td>-11.186024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-23</th>\n",
       "      <td>2.437176</td>\n",
       "      <td>2.296166</td>\n",
       "      <td>-1.950256</td>\n",
       "      <td>-9.612652</td>\n",
       "      <td>-11.044278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ewmac8   ewmac16   ewmac32    ewmac64      carry\n",
       "date                                                          \n",
       "2018-02-16  2.738041  2.023372 -2.656252 -10.260607 -11.613287\n",
       "2018-02-20  2.602314  2.078657 -2.483146 -10.103146 -11.471541\n",
       "2018-02-21  2.515616  2.143955 -2.307459  -9.942540 -11.328670\n",
       "2018-02-22  2.489667  2.230353 -2.122919  -9.775568 -11.186024\n",
       "2018-02-23  2.437176  2.296166 -1.950256  -9.612652 -11.044278"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i['corn'].forecasts().tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the returns for each rule to give us a sense of how it performs. **This is not a scientific approach**, it's just useful to get an idea of what's going on. We'll test this a little better later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-26T11:05:14.115739",
     "start_time": "2017-06-26T11:05:04.661340"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f80933945f8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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ppoaGtm/fnj///BNFUYiLiyMnJ4cuXbpw8OBBAPbu3UuHDh1o06YNR48exWAwkJGRwYUL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odufEoIOFoFRTXOMrCMinmnslgCQfQ1LZ9/CtPBA6h78fn+vhUZLoXfCGdWktMID7w7vmD6sO+p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T7Rw4nsd9P9+Mp/6yD5v2t6DN5cf3X9gldXxldBY4c1aAovXguQj8bjkS7Gp4t99zoqj+m/wM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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8093890ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i['corn'].forecast_returns().apply(np.trim_zeros).cumsum().plot(title=\"Forecast returns for each individual rule\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, **faster rules perform better**, if it weren't for costs. There is a balance between the cost of trading and performance.\n",
    "\n",
    "Equally, slower rules that don't really do any trading at all will underform good active strategies (Law of Active Management).\n",
    "\n",
    "In this particular case, our original version of the system included faster variations `ewmac2` and `ewmac4`. While these made money when prices moved quickly, the overall costs generated by trading them resulted in losses in the long term. Some instruments are very cheap to trade, and some very expensive to trade, so there's an argument for custom weights depending on instrument, but our system doesn't (yet) support it. Corn is relatively expensive to trade.\n",
    "\n",
    "### Correlations\n",
    "\n",
    "The next thing we'd like to check is the correlation of the rules. Most rules fall into one of two categories:\n",
    "* Trend following- things like EWMAC.\n",
    "* Mean-reverting - the opposite of trend following- that prices will return to a historical level.\n",
    "\n",
    "EWMAC, Carry and Breakout are all trend-following type rules; we'd expect them to be fairly correlated. We always seek to have **uncorrelated** sources of return, it means that when one thing isn't making money, something else is.\n",
    "\n",
    "The daily returns are mostly noise; we'll look at the weekly returns as this gives us a much better picture of correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-26T11:05:14.269725",
     "start_time": "2017-06-26T11:05:14.119014"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ewmac8</th>\n",
       "      <th>ewmac16</th>\n",
       "      <th>ewmac32</th>\n",
       "      <th>ewmac64</th>\n",
       "      <th>carry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ewmac8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.874343</td>\n",
       "      <td>0.621665</td>\n",
       "      <td>0.359930</td>\n",
       "      <td>0.068957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ewmac16</th>\n",
       "      <td>0.874343</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.870993</td>\n",
       "      <td>0.564875</td>\n",
       "      <td>0.226929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ewmac32</th>\n",
       "      <td>0.621665</td>\n",
       "      <td>0.870993</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.811779</td>\n",
       "      <td>0.484201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ewmac64</th>\n",
       "      <td>0.359930</td>\n",
       "      <td>0.564875</td>\n",
       "      <td>0.811779</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.730461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carry</th>\n",
       "      <td>0.068957</td>\n",
       "      <td>0.226929</td>\n",
       "      <td>0.484201</td>\n",
       "      <td>0.730461</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ewmac8   ewmac16   ewmac32   ewmac64     carry\n",
       "ewmac8   1.000000  0.874343  0.621665  0.359930  0.068957\n",
       "ewmac16  0.874343  1.000000  0.870993  0.564875  0.226929\n",
       "ewmac32  0.621665  0.870993  1.000000  0.811779  0.484201\n",
       "ewmac64  0.359930  0.564875  0.811779  1.000000  0.730461\n",
       "carry    0.068957  0.226929  0.484201  0.730461  1.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i['corn'].forecast_returns().apply(np.trim_zeros).resample('W').sum().corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So from the the correlation matrix, we can see `ewmac8` and `ewmac16` are quite correlated (which makes sense, as they cover a similar time period), but everything else not so much. In this case, we might consider dropping the `ewmac8`, but we'd need to do more research first.\n",
    "\n",
    "### Important Warning\n",
    "\n",
    "This correlation matrix is from a single instrument, which is not enough data to produce statistical significance at all. This is just a guide. Furthermore, there will be very substantial errors on these correlation numbers, which are also not shown here, and would need to be estimated via bootstrapping.\n",
    "\n",
    "#### Why not develop a portfolio wide bootstrapping framework for this stuff?\n",
    "\n",
    "We could, and we probably will, but the reality is, we usually know what the answer before we start- that is; faster rules lose money because of costs, and we are aware of the correlations because we designed the rules ourselves. \n",
    "\n",
    "The reality is, designing the system with your brain to begin with can save a lot of work.\n",
    "\n",
    "## Next Step\n",
    "\n",
    "Remove any variations (usually the faster ones) that are clearly going to lose money most of the time. There's no point feeding this to an optimizer to give us weights, as these weights have errors, and we already know that the best weight is probably zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-26T11:05:19.415225",
     "start_time": "2017-06-26T11:05:14.271690"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p_learn = portfolio.Portfolio(instruments=config.portfolios.p_learn)\n",
    "p_test = portfolio.Portfolio(instruments=config.portfolios.p_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2017-06-26T10:31:45.805Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p_learn_results = p_learn.bootstrap_pool()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2017-06-26T10:31:49.782Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p_test_results = p_test.bootstrap_pool()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2017-06-26T10:04:54.738Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p_learn_results.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2017-06-26T10:04:54.762Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>brk160</th>\n",
       "      <th>brk320</th>\n",
       "      <th>brk40</th>\n",
       "      <th>brk80</th>\n",
       "      <th>carry_next</th>\n",
       "      <th>ewmac16</th>\n",
       "      <th>ewmac32</th>\n",
       "      <th>ewmac64</th>\n",
       "      <th>ewmac8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>arabica</th>\n",
       "      <td>0.091411</td>\n",
       "      <td>0.094500</td>\n",
       "      <td>0.102534</td>\n",
       "      <td>0.070266</td>\n",
       "      <td>0.166253</td>\n",
       "      <td>0.115733</td>\n",
       "      <td>0.077644</td>\n",
       "      <td>0.121011</td>\n",
       "      <td>0.160646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aud</th>\n",
       "      <td>0.096275</td>\n",
       "      <td>0.131821</td>\n",
       "      <td>0.114340</td>\n",
       "      <td>0.095504</td>\n",
       "      <td>0.103970</td>\n",
       "      <td>0.079317</td>\n",
       "      <td>0.098211</td>\n",
       "      <td>0.177893</td>\n",
       "      <td>0.102669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bobl</th>\n",
       "      <td>0.135884</td>\n",
       "      <td>0.189463</td>\n",
       "      <td>0.093324</td>\n",
       "      <td>0.095771</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.110144</td>\n",
       "      <td>0.129083</td>\n",
       "      <td>0.109820</td>\n",
       "      <td>0.136510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cattle</th>\n",
       "      <td>0.102840</td>\n",
       "      <td>0.185989</td>\n",
       "      <td>0.085301</td>\n",
       "      <td>0.113051</td>\n",
       "      <td>0.109736</td>\n",
       "      <td>0.080635</td>\n",
       "      <td>0.108482</td>\n",
       "      <td>0.138746</td>\n",
       "      <td>0.075221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cocoa</th>\n",
       "      <td>0.111439</td>\n",
       "      <td>0.140892</td>\n",
       "      <td>0.113047</td>\n",
       "      <td>0.090367</td>\n",
       "      <td>0.168859</td>\n",
       "      <td>0.068633</td>\n",
       "      <td>0.097852</td>\n",
       "      <td>0.131121</td>\n",
       "      <td>0.077790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crude</th>\n",
       "      <td>0.089997</td>\n",
       "      <td>0.209064</td>\n",
       "      <td>0.092846</td>\n",
       "      <td>0.106617</td>\n",
       "      <td>0.095509</td>\n",
       "      <td>0.062421</td>\n",
       "      <td>0.074637</td>\n",
       "      <td>0.116058</td>\n",
       "      <td>0.152852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dax</th>\n",
       "      <td>0.107493</td>\n",
       "      <td>0.147770</td>\n",
       "      <td>0.126555</td>\n",
       "      <td>0.101628</td>\n",
       "      <td>0.161106</td>\n",
       "      <td>0.063289</td>\n",
       "      <td>0.080811</td>\n",
       "      <td>0.121831</td>\n",
       "      <td>0.089518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eurodollar</th>\n",
       "      <td>0.090753</td>\n",
       "      <td>0.131421</td>\n",
       "      <td>0.108798</td>\n",
       "      <td>0.119241</td>\n",
       "      <td>0.155378</td>\n",
       "      <td>0.078037</td>\n",
       "      <td>0.117951</td>\n",
       "      <td>0.082770</td>\n",
       "      <td>0.115653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eurostoxx</th>\n",
       "      <td>0.100105</td>\n",
       "      <td>0.140422</td>\n",
       "      <td>0.135728</td>\n",
       "      <td>0.099953</td>\n",
       "      <td>0.133559</td>\n",
       "      <td>0.087040</td>\n",
       "      <td>0.128602</td>\n",
       "      <td>0.094811</td>\n",
       "      <td>0.079782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder</th>\n",
       "      <td>0.077399</td>\n",
       "      <td>0.131338</td>\n",
       "      <td>0.097178</td>\n",
       "      <td>0.099070</td>\n",
       "      <td>0.192704</td>\n",
       "      <td>0.077128</td>\n",
       "      <td>0.078603</td>\n",
       "      <td>0.137700</td>\n",
       "      <td>0.108880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftse</th>\n",
       "      <td>0.142898</td>\n",
       "      <td>0.136968</td>\n",
       "      <td>0.131359</td>\n",
       "      <td>0.129577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.128309</td>\n",
       "      <td>0.132605</td>\n",
       "      <td>0.088960</td>\n",
       "      <td>0.109325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gas</th>\n",
       "      <td>0.150929</td>\n",
       "      <td>0.115912</td>\n",
       "      <td>0.112157</td>\n",
       "      <td>0.062611</td>\n",
       "      <td>0.111925</td>\n",
       "      <td>0.098362</td>\n",
       "      <td>0.139359</td>\n",
       "      <td>0.094952</td>\n",
       "      <td>0.113795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gbp</th>\n",
       "      <td>0.083294</td>\n",
       "      <td>0.167725</td>\n",
       "      <td>0.117409</td>\n",
       "      <td>0.104699</td>\n",
       "      <td>0.108099</td>\n",
       "      <td>0.090968</td>\n",
       "      <td>0.079751</td>\n",
       "      <td>0.120466</td>\n",
       "      <td>0.127589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leanhog</th>\n",
       "      <td>0.111053</td>\n",
       "      <td>0.138623</td>\n",
       "      <td>0.169352</td>\n",
       "      <td>0.108429</td>\n",
       "      <td>0.158587</td>\n",
       "      <td>0.087295</td>\n",
       "      <td>0.101272</td>\n",
       "      <td>0.072812</td>\n",
       "      <td>0.052577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longbtp</th>\n",
       "      <td>0.128329</td>\n",
       "      <td>0.098947</td>\n",
       "      <td>0.091504</td>\n",
       "      <td>0.112530</td>\n",
       "      <td>0.096889</td>\n",
       "      <td>0.076860</td>\n",
       "      <td>0.085214</td>\n",
       "      <td>0.142528</td>\n",
       "      <td>0.167199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pallad</th>\n",
       "      <td>0.121911</td>\n",
       "      <td>0.092030</td>\n",
       "      <td>0.085919</td>\n",
       "      <td>0.083292</td>\n",
       "      <td>0.180907</td>\n",
       "      <td>0.101955</td>\n",
       "      <td>0.087628</td>\n",
       "      <td>0.115499</td>\n",
       "      <td>0.130859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>robusta</th>\n",
       "      <td>0.111514</td>\n",
       "      <td>0.145218</td>\n",
       "      <td>0.093084</td>\n",
       "      <td>0.099308</td>\n",
       "      <td>0.145931</td>\n",
       "      <td>0.078805</td>\n",
       "      <td>0.076722</td>\n",
       "      <td>0.134421</td>\n",
       "      <td>0.114998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smi</th>\n",
       "      <td>0.145296</td>\n",
       "      <td>0.105334</td>\n",
       "      <td>0.067771</td>\n",
       "      <td>0.084661</td>\n",
       "      <td>0.142070</td>\n",
       "      <td>0.072896</td>\n",
       "      <td>0.159453</td>\n",
       "      <td>0.141717</td>\n",
       "      <td>0.080802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>soybean</th>\n",
       "      <td>0.157204</td>\n",
       "      <td>0.120291</td>\n",
       "      <td>0.142224</td>\n",
       "      <td>0.083638</td>\n",
       "      <td>0.114648</td>\n",
       "      <td>0.076036</td>\n",
       "      <td>0.073537</td>\n",
       "      <td>0.125669</td>\n",
       "      <td>0.106769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>us30</th>\n",
       "      <td>0.125748</td>\n",
       "      <td>0.102637</td>\n",
       "      <td>0.152398</td>\n",
       "      <td>0.115299</td>\n",
       "      <td>0.153672</td>\n",
       "      <td>0.082811</td>\n",
       "      <td>0.095063</td>\n",
       "      <td>0.089730</td>\n",
       "      <td>0.082642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vix</th>\n",
       "      <td>0.112296</td>\n",
       "      <td>0.159083</td>\n",
       "      <td>0.110360</td>\n",
       "      <td>0.122845</td>\n",
       "      <td>0.111706</td>\n",
       "      <td>0.123430</td>\n",
       "      <td>0.084657</td>\n",
       "      <td>0.104786</td>\n",
       "      <td>0.070837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wheat</th>\n",
       "      <td>0.121514</td>\n",
       "      <td>0.108370</td>\n",
       "      <td>0.158987</td>\n",
       "      <td>0.093691</td>\n",
       "      <td>0.163630</td>\n",
       "      <td>0.092511</td>\n",
       "      <td>0.080803</td>\n",
       "      <td>0.070343</td>\n",
       "      <td>0.110152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yen</th>\n",
       "      <td>0.089516</td>\n",
       "      <td>0.103546</td>\n",
       "      <td>0.167544</td>\n",
       "      <td>0.092789</td>\n",
       "      <td>0.128057</td>\n",
       "      <td>0.093170</td>\n",
       "      <td>0.093114</td>\n",
       "      <td>0.120870</td>\n",
       "      <td>0.111395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              brk160    brk320     brk40     brk80  carry_next   ewmac16  \\\n",
       "arabica     0.091411  0.094500  0.102534  0.070266    0.166253  0.115733   \n",
       "aud         0.096275  0.131821  0.114340  0.095504    0.103970  0.079317   \n",
       "bobl        0.135884  0.189463  0.093324  0.095771         NaN  0.110144   \n",
       "cattle      0.102840  0.185989  0.085301  0.113051    0.109736  0.080635   \n",
       "cocoa       0.111439  0.140892  0.113047  0.090367    0.168859  0.068633   \n",
       "crude       0.089997  0.209064  0.092846  0.106617    0.095509  0.062421   \n",
       "dax         0.107493  0.147770  0.126555  0.101628    0.161106  0.063289   \n",
       "eurodollar  0.090753  0.131421  0.108798  0.119241    0.155378  0.078037   \n",
       "eurostoxx   0.100105  0.140422  0.135728  0.099953    0.133559  0.087040   \n",
       "feeder      0.077399  0.131338  0.097178  0.099070    0.192704  0.077128   \n",
       "ftse        0.142898  0.136968  0.131359  0.129577         NaN  0.128309   \n",
       "gas         0.150929  0.115912  0.112157  0.062611    0.111925  0.098362   \n",
       "gbp         0.083294  0.167725  0.117409  0.104699    0.108099  0.090968   \n",
       "leanhog     0.111053  0.138623  0.169352  0.108429    0.158587  0.087295   \n",
       "longbtp     0.128329  0.098947  0.091504  0.112530    0.096889  0.076860   \n",
       "pallad      0.121911  0.092030  0.085919  0.083292    0.180907  0.101955   \n",
       "robusta     0.111514  0.145218  0.093084  0.099308    0.145931  0.078805   \n",
       "smi         0.145296  0.105334  0.067771  0.084661    0.142070  0.072896   \n",
       "soybean     0.157204  0.120291  0.142224  0.083638    0.114648  0.076036   \n",
       "us30        0.125748  0.102637  0.152398  0.115299    0.153672  0.082811   \n",
       "vix         0.112296  0.159083  0.110360  0.122845    0.111706  0.123430   \n",
       "wheat       0.121514  0.108370  0.158987  0.093691    0.163630  0.092511   \n",
       "yen         0.089516  0.103546  0.167544  0.092789    0.128057  0.093170   \n",
       "\n",
       "             ewmac32   ewmac64    ewmac8  \n",
       "arabica     0.077644  0.121011  0.160646  \n",
       "aud         0.098211  0.177893  0.102669  \n",
       "bobl        0.129083  0.109820  0.136510  \n",
       "cattle      0.108482  0.138746  0.075221  \n",
       "cocoa       0.097852  0.131121  0.077790  \n",
       "crude       0.074637  0.116058  0.152852  \n",
       "dax         0.080811  0.121831  0.089518  \n",
       "eurodollar  0.117951  0.082770  0.115653  \n",
       "eurostoxx   0.128602  0.094811  0.079782  \n",
       "feeder      0.078603  0.137700  0.108880  \n",
       "ftse        0.132605  0.088960  0.109325  \n",
       "gas         0.139359  0.094952  0.113795  \n",
       "gbp         0.079751  0.120466  0.127589  \n",
       "leanhog     0.101272  0.072812  0.052577  \n",
       "longbtp     0.085214  0.142528  0.167199  \n",
       "pallad      0.087628  0.115499  0.130859  \n",
       "robusta     0.076722  0.134421  0.114998  \n",
       "smi         0.159453  0.141717  0.080802  \n",
       "soybean     0.073537  0.125669  0.106769  \n",
       "us30        0.095063  0.089730  0.082642  \n",
       "vix         0.084657  0.104786  0.070837  \n",
       "wheat       0.080803  0.070343  0.110152  \n",
       "yen         0.093114  0.120870  0.111395  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_test_results.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-26T11:53:24.369506",
     "start_time": "2017-06-26T11:53:23.839525"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f940d604a58>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kzAEAABRImAMAACiQMAcAAFAgYQ4AAKBAwhwAAECBhDkAAIACCXMAAAAFEuYAAAAKJMwB\nAAAUSJgDAAAokDAHAABQIGEOAACgQANqXQAAbK4+ePBFtS4BAN6SK3MAAAAF6tdX5k7/2j19ctzO\nrnV9evybp47rk+MCAADlcGUOAACgQMIcAABAgYQ5AACAAglzAAAABRLmAAAACiTMAQAAFEiYAwAA\nKJAwBwAAUCBhDgAAoEDCHAAAQIGEOQAAgAIJcwAAAAUS5gAAAAokzAEAABRoQK0LAPrW6V+7p0+O\n29m1rk+Pf/PUcX1yXACAeuHKHAAAQIGEOQAAgAIZZkld+ODBF9W6BAAAqCpX5gAAAAokzAEAABRI\nmAMAACiQMAcAAFAgYQ4AAKBAwhwAAECBhDkAAIACCXMAAAAFEuYAAAAKJMwBAAAUaECtCwCoN6d/\n7Z4+OW5n17o+Pf7NU8f1yXEBgL7hyhwAAECBhDkAAIACCXMAAAAFEuYAAAAKJMwBAAAUSJgDAAAo\n0CY9mmDWrFl59NFH09DQkGnTpmXMmDGVbS+//HKmT5+epUuXZsGCBW+7zzPPPJMLLrggGzZsSHNz\nc6644opsueWWvX9WAAAAda7HK3OLFy/O8uXL09bWlssuuyyXXXbZRtvnzJmTUaNGbdI+8+bNy8SJ\nE/Pd7343u+yyS+bPn9+LpwIAANB/9Bjm2tvbM378+CTJrrvumq6urqxZs6ayffLkyZXtPe2zaNGi\nHHzwwUmSsWPHpr29vddOBAAAoD/pMcx1dnamqampsjx06NB0dHRUlgcNGrTJ+6xdu7YyrHK77bbb\n6DgAAABsuk26Z+71uru73/E3ebN9NuU4TU0DM2BA4zv+fvWuuXlwrUvod/S8+vS8+vS8+vT8relN\n9TQ2vva3fT2vH36Wb1Sv7/Mew1xLS0s6Ozsry6tWrUpzc/O72mfgwIFZt25dttpqq6xcuTItLS1v\ne5zVq1/qqbx+qaPjxVqX0O/oefXpefXpefXp+Ztrbh6sN1W0YcOraWzcQs/riJ/lG5X8Pn+7ANpj\nmGttbc0111yTE088MUuWLElLS8ubDq3clH3233//LFy4MEcddVTuuuuuHHDAAe/8bAAA+qHTv3ZP\nnxy3s2tdnx7/5qnj+uS4wCaEuT333DOjR4/OiSeemIaGhsyYMSMLFizI4MGDc8ghh+Tcc8/Ns88+\nmyeffDKnnHJKjj/++EyYMOEN+yTJpEmTcuGFF6atrS077rhjjj766D4/QQAAgHq0SffMTZkyZaPl\nkSNHVr6eN2/eJu2TvDb88pZbbnkn9QEAAPAmepzNEgAAgM2PMAcAAFAgYQ4A2Gwce+yEjBtnwgyA\nTSHMAQAAFEiYAwAAKJAwBwAAUCBhDgAAoEDCHAAAQIGEOQAAgAIJcwAAAAUaUOsCAAAAkuT0r93T\nJ8ft7FrXp8e/eWptno/pyhwAAECBhDkAAIACCXMAAAAFEuYAAAAKJMwBAAAUSJgDAAAokDAHAABQ\nIGEOAACgQMIcAABAgYQ5AACAAglzAAAABRLmAAAACiTMAQAAFEiYAwAAKJAwBwAAUCBhDgAAoEDC\nHAAAQIGEOQAAgAIJcwAAAAUS5gAAAAokzAEAABRImAMAACiQMAcAAFAgYQ4AAKBAwhwAAECBhDkA\nAIACCXMAAAAFEuYAAAAKJMwBAAAUSJgDAAAokDAHAABQIGEOAACgQMIcAABAgYQ5AACAAglzAAAA\nBRLmAAAACjSg1gUAAEB/8sGDL6p1CdQJYa4P+A8UAADoa4ZZAgAAFEiYAwAAKJBhlgDAO3b61+7p\nk+N2dq3r0+PfPHVcnxwXoBZcmQMAACiQMAcAAFAgYQ4AAKBAwhwAAECBhDkAAIACCXMAAAAFEuYA\nAAAKJMwBAAAUSJgDAAAo0IBaFwAAQO188OCLal0C8C65MgcAAFAgYQ4AAKBAwhwAAECBNumeuVmz\nZuXRRx9NQ0NDpk2bljFjxlS2Pfjgg5k7d24aGxtz4IEH5uyzz87tt9+eO++8s/KaX/3qV3nkkUcy\nderULFmyJEOGDEmSnHHGGfnUpz7Vu2cEUKfc1wIAvF6PYW7x4sVZvnx52trasmzZskybNi1tbW2V\n7ZdeemluuummDBs2LCeffHIOO+ywHHfccTnuuOMq+//4xz+uvP7888/P2LFj++BUAAAA+o8eh1m2\nt7dn/PjxSZJdd901XV1dWbNmTZJkxYoV2XbbbTN8+PBsscUWOeigg9Le3r7R/tddd12++MUv9kHp\nAAAA/VePYa6zszNNTU2V5aFDh6ajoyNJ0tHRkaFDh77ptiR57LHHMnz48DQ3N1fW3XbbbTn11FMz\nefLkPP/8871yEgAAAP3NO37OXHd39ya/dv78+TnmmGMqy0cddVSGDBmSUaNG5YYbbsi1116b6dOn\nv+X+TU0DM2BA4zstse41Nw+udQn9jp5Xn55Xn55Xn55Xn55Xn55Xn55XX6163mOYa2lpSWdnZ2V5\n1apVlStt/3HbypUr09LSUlletGhRvvrVr1aW99tvv8rX48aNy8yZM9/2e69e/VLPZ9APdXS8WOsS\n+h09rz49rz49rz49rz49rz49rz49r76+7PnbBcUeh1m2trZm4cKFSZIlS5akpaUlgwYNSpLstNNO\nWbNmTZ566qmsX78+9957b1pbW5O8Fuze9773Zcstt6wca9KkSVmxYkWS14Lebrvt9u7PCgAAoB/r\n8crcnnvumdGjR+fEE09MQ0NDZsyYkQULFmTw4ME55JBDMnPmzHz5y19OkhxxxBEZMWJEkjfeT5ck\nJ510Us4777xsvfXWGThwYGbPnt0HpwQAAFD/NumeuSlTpmy0PHLkyMrXe++990aPKviLj370o7nx\nxhs3WrfvvvvmjjvueDd1AgAA8Do9DrMEAABg8yPMAQAAFEiYAwAAKJAwBwAAUCBhDgAAoEDCHAAA\nQIGEOQAAgAIJcwAAAAXapIeGA/xHHzz4olqXAADQr7kyBwAAUCBhDgAAoEDCHAAAQIGEOQAAgAIJ\ncwAAAAUS5gAAAAokzAEAABRImAMAACiQMAcAAFAgYQ4AAKBAwhwAAECBhDkAAIACCXMAAAAFEuYA\nAAAKJMwBAAAUSJgDAAAo0IBaFwAA8BcfPPiiWpcAUAxX5gAAAAokzAEAABRImAMAACiQMAcAAFAg\nE6AAAAB1rV4nV3JlDgAAoEDCHAAAQIGEOQAAgAIJcwAAAAUS5gAAAAokzAEAABRImAMAACiQMAcA\nAFAgYQ4AAKBAwhwAAECBhDkAAIACCXMAAAAFEuYAAAAKJMwBAAAUSJgDAAAokDAHAABQIGEOAACg\nQMIcAABAgYQ5AACAAglzAAAABRLmAAAACiTMAQAAFEiYAwAAKJAwBwAAUCBhDgAAoEDCHAAAQIGE\nOQAAgAIJcwAAAAUS5gAAAAokzAEAABRImAMAACiQMAcAAFAgYQ4AAKBAwhwAAECBBmzKi2bNmpVH\nH300DQ0NmTZtWsaMGVPZ9uCDD2bu3LlpbGzMgQcemLPPPjuLFi3Kl770pey2225Jkt133z2XXHJJ\nnnnmmVxwwQXZsGFDmpubc8UVV2TLLbfsmzMDAACoYz2GucWLF2f58uVpa2vLsmXLMm3atLS1tVW2\nX3rppbnpppsybNiwnHzyyTnssMOSJPvss0/mzZu30bHmzZuXiRMn5vDDD8/cuXMzf/78TJw4sZdP\nCQAAoP71OMyyvb0948ePT5Lsuuuu6erqypo1a5IkK1asyLbbbpvhw4dniy22yEEHHZT29va3PNai\nRYty8MEHJ0nGjh37tq8FAADgrfV4Za6zszOjR4+uLA8dOjQdHR0ZNGhQOjo6MnTo0I22rVixIrvv\nvnueeOKJnHXWWenq6so555yT1tbWrF27tjKscrvttktHR8fbfu+mpoEZMKDx3Z5b3WpuHlzrEvod\nPa8+Pa8+Pa8+Pa8+Pa8+Pa8+Pa++WvV8k+6Ze73u7u4eX/OBD3wg55xzTg4//PCsWLEip556au66\n6653fJzVq196p+X1Cx0dL9a6hH5Hz6tPz6tPz6tPz6tPz6tPz6tPz6uvL3v+dkGxx2GWLS0t6ezs\nrCyvWrUqzc3Nb7pt5cqVaWlpybBhw3LEEUekoaEhO++8c7bffvusXLkyAwcOzLp16zZ6LQAAAO9c\nj2GutbU1CxcuTJIsWbIkLS0tGTRoUJJkp512ypo1a/LUU09l/fr1uffee9Pa2po777wzN910U5Kk\no6Mjzz33XIYNG5b999+/cqy77rorBxxwQF+dFwAAQF3rcZjlnnvumdGjR+fEE09MQ0NDZsyYkQUL\nFmTw4ME55JBDMnPmzHz5y19OkhxxxBEZMWJEmpubM2XKlNx999155ZVXMnPmzGy55ZaZNGlSLrzw\nwrS1tWXHHXfM0Ucf3ecnCAAAUI826Z65KVOmbLQ8cuTIytd77733Ro8qSJJBgwbl+uuvf8NxWlpa\ncsstt7ybOgEAAHidHodZAgAAsPkR5gAAAAokzAEAABRImAMAACiQMAcAAFAgYQ4AAKBAwhwAAECB\nhDkAAIACCXMAAAAFEuYAAAAKJMwBAAAUSJgDAAAokDAHAABQIGEOAACgQMIcAABAgYQ5AACAAglz\nAAAABRLmAAAACiTMAQAAFEiYAwAAKJAwBwAAUCBhDgAAoEDCHAAAQIGEOQAAgAIJcwAAAAUS5gAA\nAAokzAEAABRImAMAACiQMAcAAFAgYQ4AAKBAwhwAAECBhDkAAIACCXMAAAAFEuYAAAAKJMwBAAAU\nSJgDAAAokDAHAABQIGEOAACgQMIcAABAgYQ5AACAAglzAAAABRLmAAAACiTMAQAAFEiYAwAAKJAw\nBwAAUCBhDgAAoEDCHAAAQIGEOQAAgAIJcwAAAAUS5gAAAAokzAEAABRImAMAACiQMAcAAFAgYQ4A\nAKBAwhwAAECBhDkAAIACCXMAAAAFEuYAAAAKJMwBAAAUSJgDAAAokDAHAABQIGEOAACgQMIcAABA\ngYQ5AACAAglzAAAABRqwKS+aNWtWHn300TQ0NGTatGkZM2ZMZduDDz6YuXPnprGxMQceeGDOPvvs\nJMmcOXPy0EMPZf369fnCF76QQw89NFOnTs2SJUsyZMiQJMkZZ5yRT33qU71/VgAAAHWuxzC3ePHi\nLF++PG1tbVm2bFmmTZuWtra2yvZLL700N910U4YNG5aTTz45hx12WDo7O7N06dK0tbVl9erVOeaY\nY3LooYcmSc4///yMHTu2784IAACgH+gxzLW3t2f8+PFJkl133TVdXV1Zs2ZNBg0alBUrVmTbbbfN\n8OHDkyQHHXRQ2tvbM3HixMrVu2222SZr167Nhg0b+vA0AAAA+pcew1xnZ2dGjx5dWR46dGg6Ojoy\naNCgdHR0ZOjQoRttW7FiRRobGzNw4MAkyfz583PggQemsbExSXLbbbfllltuyXbbbZdLLrlko/3/\no6amgRkwoPFdn1y9am4eXOsS+h09rz49rz49rz49rz49rz49rz49r75a9XyT7pl7ve7u7k1+7U9+\n8pPMnz8/N998c5LkqKOOypAhQzJq1KjccMMNufbaazN9+vS33H/16pfeaXn9QkfHi7Uuod/R8+rT\n8+rT8+rT8+rT8+rT8+rT8+rry56/XVDscTbLlpaWdHZ2VpZXrVqV5ubmN922cuXKtLS0JEnuv//+\nXH/99fn2t7+dwYNfK2C//fbLqFGjkiTjxo3Lv/3bv72L0wEAAKDHMNfa2pqFCxcmSZYsWZKWlpYM\nGjQoSbLTTjtlzZo1eeqpp7J+/frce++9aW1tzYsvvpg5c+bkW9/6VmXmyiSZNGlSVqxYkSRZtGhR\ndtttt744JwAAgLrX4zDLPffcM6NHj86JJ56YhoaGzJgxIwsWLMjgwYNzyCGHZObMmfnyl7+cJDni\niCMyYsSIyiyW5513XuU4l19+eU466aScd9552XrrrTNw4MDMnj27784MAACgjm3SPXNTpkzZaHnk\nyJGVr/fee++NHlWQJCeccEJOOOGENxxnxx13zB133PFu6gQAAOB1ehxmCQAAwOZHmAMAACiQMAcA\nAFAgYQ4AAKBAwhwAAECBhDkAAIACCXMAAAAFEuYAAAAKJMwBAAAUSJgDAAAokDAHAABQIGEOAACg\nQMIcAAAnbl24AAAZYklEQVRAgYQ5AACAAglzAAAABRLmAAAACiTMAQAAFEiYAwAAKJAwBwAAUCBh\nDgAAoEDCHAAAQIGEOQAAgAIJcwAAAAUS5gAAAAokzAEAABRImAMAACiQMAcAAFAgYQ4AAKBAwhwA\nAECBhDkAAIACCXMAAAAFEuYAAAAKJMwBAAAUSJgDAAAokDAHAABQIGEOAACgQMIcAABAgYQ5AACA\nAglzAAAABRLmAAAACiTMAQAAFEiYAwAAKJAwBwAAUCBhDgAAoEDCHAAAQIGEOQAAgAIJcwAAAAUS\n5gAAAAokzAEAABRImAMAACiQMAcAAFAgYQ4AAKBAwhwAAECBhDkAAIACCXMAAAAFEuYAAAAKJMwB\nAAAUSJgDAAAokDAHAABQIGEOAACgQMIcAABAgYQ5AACAAglzAAAABRLmAAAACiTMAQAAFGjAprxo\n1qxZefTRR9PQ0JBp06ZlzJgxlW0PPvhg5s6dm8bGxhx44IE5++yz33KfZ555JhdccEE2bNiQ5ubm\nXHHFFdlyyy375swAAADqWI9X5hYvXpzly5enra0tl112WS677LKNtl966aW55ppr8r3vfS8PPPBA\nnnjiibfcZ968eZk4cWK++93vZpdddsn8+fP75qwAAADqXI9hrr29PePHj0+S7Lrrrunq6sqaNWuS\nJCtWrMi2226b4cOHZ4sttshBBx2U9vb2t9xn0aJFOfjgg5MkY8eOTXt7e1+dFwAAQF3rcZhlZ2dn\nRo8eXVkeOnRoOjo6MmjQoHR0dGTo0KEbbVuxYkVWr179pvusXbu2Mqxyu+22S0dHx9t+7+bmwe/4\nhN6JH1x1VJ8enzfS8+rT8+rT8+rT8+rT8+rT8+rT8+rT83fmHU+A0t3d/Y6/yZvt826OAwAAwGt6\nvDLX0tKSzs7OyvKqVavS3Nz8pttWrlyZlpaWvOc973nTfQYOHJh169Zlq622qrwWAACAd67HK3Ot\nra1ZuHBhkmTJkiVpaWnJoEGDkiQ77bRT1qxZk6eeeirr16/Pvffem9bW1rfcZ//996+sv+uuu3LA\nAQf01XkBAADUtYbuTRjveOWVV+YXv/hFGhoaMmPGjPz617/O4MGDc8ghh+TnP/95rrzyyiTJoYce\nmjPOOONN9xk5cmRWrVqVCy+8MC+//HJ23HHHzJ49O+95z3v69gwBAADq0CaFOQAAADYv73gCFAAA\nAGpPmAMAACiQMAcAAFAgYa4X/elPf8ry5cuzfPnyvPTSS7Uup1/Qc6C33XvvvW9Y98Mf/rAGlQDA\n2+vxOXP07PHHH89ll12WP/7xj2lqakp3d3dWrVqVYcOGZfr06fnwhz9c6xLrjp7XxrJly/Kzn/0s\nq1atSvLasyY/+clPZpdddqlxZfVLz6vnsccey+OPP55bb701Tz/9dGX9+vXrc9NNN+XII4+sYXX1\n6+WXX8573/veJK+935cuXZoRI0b4Pd6Hli1blhdeeCEf//jH09jYWFl/7733ZuzYsTWsrP+46aab\nKjPA0/tefPHF/NM//VOamprymc98Jv/4j/+YJUuWZJdddskpp5xSecxaPTCbZS/47Gc/m0svvTS7\n7rrrRuuXLFmSWbNm5R//8R9rVFn90vPq+8Y3vpEHHnggBx10UIYOHZru7u6sXLkyP/3pT3PkkUfm\ntNNOq3WJdUfPq+uZZ57Jz372s1xzzTX5zGc+U1nf0NCQPfbYI/vvv38Nq6tP3/jGN7Js2bJcddVV\n+c53vpMFCxZkr732ym9+85u0trbmnHPOqXWJdefrX/962tvbM2TIkDz99NO56qqrKsH51FNPza23\n3lrjCuvPRRdd9IZ19913Xw466KAkyezZs6tdUt0766yzsscee6SrqysPP/xw9txzz+y///55/PHH\n85vf/Cbz5s2rdYm9xpW5XtDd3f2GUJEko0ePzoYNG2pQUf3T8+r713/913zve99LQ0PDRuvPOuus\nnHzyyYJFH9Dz6ho+fHiOOeaYvP/9789//s//eaNtCxcurFFV9e2ee+7J/Pnzk7zW47a2tmy11VZ5\n9dVXM3HiRGGuDyxatCj//M//nCT53e9+lylTpuSKK67IyJEj4+/7fePll1/OU089lb/7u7/L+973\nvnR3d+exxx7LMcccU+vS6tZLL72UL3zhC0mSww8/PBdccEGS5JOf/GROPfXUWpbW64S5XvBXf/VX\nOeusszJ+/PgMHTo0SdLZ2ZmFCxdmn332qXF19enter733nvXuLr6tGHDhspQ1tf7y/A/ep+e18Y3\nv/nN/Kf/9J9ywQUX5IUXXsh//a//NU1NTTnssMNqXVrd6e7uzm9/+9uMHDkyu+yyS/785z9nq622\nypo1a/Lqq6/Wury6tGHDhqxduzZbb711PvzhD+eaa67JpEmTMmPGjDf84YjeMXfu3DzwwAO5/vrr\n89nPfjZHHHFEBg8e7DNiH1q/fn2WL1+e559/Pl1dXfnlL3+Zj3/841m2bFleeeWVWpfXqwyz7CU/\n//nP097ens7OziSv3dfS2tqaPfbYo8aV1S89r64HH3wwl112WYYMGVIJ0B0dHfnTn/6UGTNmvOFK\nBv//9Lx27r333syePTtbbrllZs2alTFjxtS6pLr029/+NtOnT8/atWuzzTbb5A9/+EM+9KEP5U9/\n+lMuuugife8DP/rRj3LVVVflBz/4QQYOHJgkefbZZ3PxxRfn4YcfziOPPFLjCuvXn//859xwww35\n1a9+lWeeeSbf//73a11S3frFL36R2bNnZ+jQoZk6dWouvfTS/O53v0tzc3NmzpxZV58Vhble8uyz\nz2aHHXZI8to46CeeeCIjRozIuHHjalxZ/frXf/3XvPDCCznooIOy7bbbVtbffvvtOe6442pYWX1b\nsWJFJUC/973vzUc+8pEaV1T/Xt/zYcOGZccdd6xxRfXtV7/6Va666qp8/OMfT2dnZ9asWZOvfOUr\n+t6HnnvuufzhD39Id3d3tttuu+y00061LqmurVu3LltttdUb1v/7v/97PvCBD1S/oH5m+fLlue++\n++puuN/m6plnnsnw4cOTvDb5z5vdplMyYa4XzJw5M42Njbnkkkvy9a9/Pb/+9a+z7777ZsmSJRk8\neHD+/u//vtYl1p2LL744a9asydChQ/Pggw9m5syZ2W+//ZK4gbuvvH52v+S14VGTJk3Kddddl+7u\nbh90+8Af/vCHfPvb305TU1POOOOMXHvttZXZuM4555zKH5DoXaeffnouueSSjBgxIkny8MMP5/LL\nL09bW1uNK+tfrrzyykyZMqXWZdSd1atX5/bbb8+wYcNy1FFH5Vvf+lYefvjhjBgxImeeeWZlFAC9\n58UXX8wvfvGLjB07Nn/84x9z/fXX54knnsgHP/hBPe9jV1xxRZ577rl87WtfS/La58dtt922cg9d\nPRDmesHxxx9fuZl44sSJue2227LFFq89wu+zn/1svve979WyvLo0ceLEfPe7303y2v1Df/d3f5fz\nzz8/ra2tOeWUU/IP//APNa6w/nz0ox/NBz7wgcqsikny61//Oh/5yEfS0NAgQPeB008/PZ/5zGfS\n1dWVW2+9Naeeemr233//PPbYY/nBD36QG2+8sdYl1q0///nPWbVqVeUK0YYNGzaawp3esXbt2rfc\n9vnPfz633XZbFavpHz7/+c/nr/7qr7Jq1ao899xzGTFiRA499NA89thj+elPf+r3Sh/43Oc+lyOO\nOCLHHXdcvvzlL+dDH/pQPvnJT2bJkiW5++678+1vf7vWJdat139e/IuTTjqprmY9NwFKLxgwYEDu\nvvvujBs3Lh/5yEfy9NNPZ6eddsrvf/97NxP3kb9MDNHS0pKWlpbccMMN+fznP5/nn39ez/vIggUL\nMmfOnOy///457bTTssUWW+SEE04QnPvQ+vXrK882u+2223LSSSclSUaMGJE77rijlqXVtR/96Ef5\nxje+keS1h4Vfeuml+ehHP5qjjz66xpXVn7333jstLS0brWtoaEh3d3eee+65GlVV315++eWcc845\n6e7uzl//9V/nuuuuS5KMGTPGrK19ZM2aNZXbP1atWpWrrroqSfKxj30sd955Zy1Lq3uvvvpqli5d\nmt122y3Ja88TrbfrWFvUuoB6MHfu3PzgBz/IgQcemPvvvz8TJkzIkUcemVmzZlUu69K7Jk+enFNO\nOSV/+tOfkiTbbbddbr311ixatCi//OUva1xdfdp9991z4403Ztttt81pp52Whx56SHCuggceeCA/\n/OEP8/LLL+d//s//ma6urtx33321Lquu3XbbbVmwYEGampqSJF/5ylfe8JddescFF1yQCRMm5J57\n7qn8u/vuu3PPPfeY/KSPrF+/Pn/4wx/S0NCQr371q5X1v/3tb+tulr/Nxc4775xZs2bl8ccfzyc+\n8Yn8+Mc/TmdnZxYsWJDm5uZal1fXZsyYkZkzZ2b//ffPJz/5ycydO7fubn8yzLIXdXd35/nnn093\nd3eampoMyamRt7qxm97zwgsv5Morr0x7e3vuvvvuWpdTt5544onMmzcvQ4cOzbnnnpu5c+fm4Ycf\nzi677JILLrigck8XvesvQ7X/cv9td3d3TjjhhMpwenrXv/zLv+TQQw+tzKz4F9dff33OOuusGlVV\nvx555JF85zvfyX/7b/+tsu4nP/lJrrnmmsyaNSujR4+uYXX1af369fnnf/7n3HvvvZWJfrbffvsc\neOCBOeWUU3xmqbJvfOMb+eIXv1jrMnqNMNfHpk6d6upcH/j973+fG2+8MTvssENOP/30zJo1q3ID\n91e+8pXsvPPOtS6xbi1evPgNz8b5h3/4h5xyyik1qqj+6Xl1ff3rX8/TTz+dxx57LH/zN3+Te+65\nJ5/4xCcyefLkWpdW114/49z//b//Nx/84AdrXFH9q/dZ/jZHTz/9dGXCMO/zvnfffffl6quvTldX\nV5LklVdeyQ477FBXf5wzzLIXPPHEE2/6b+nSpVm2bFmty6tLX/3qV7PnnnumoaEhp512WkaNGpVb\nb701xxxzTC6++OJal1fXvvnNb+b2229P8lqoPvnkk73P+5ieV9fkyZNzwgkn5Nhjj8173/veXHjh\nhYJcH5szZ06uvvrqyvJNN92UK664ooYV1b//2PObb745c+bMqWFF9e+KK67IvHnzKss33XSTnvex\na665JldffXV22GGHzJ8/P2effXbdPRLClblesMcee2TkyJEZMOCN88n87ne/y+LFi2tQVX17/eMH\nPv3pT290A7HZLPvW+vXrM3v27DzzzDNZsWJFvvrVr+YTn/hErcuqa3peXc8++2zuuuuuvPjiixvd\nKH/OOefUsKr61h9mnNvc6Hn16Xn1/eUz4etnl//c5z6XW265pcaV9R6zWfaCWbNm5f7778+sWbPe\nsM0wqL7z0EMPZa+99qr8VWvDhg259957TcrRR14/6cYBBxyQ//E//kdGjBiRdevW5b777stBBx1U\nw+rqk57XxllnnZUDDjjAc/yqqD/MOLe50fPq0/PqGzZsWP7lX/4lH/nIRzJlypTstNNOdTdTritz\nveTnP/95Ro8e/YYbuOfPn59jjz22RlXVr3//93/P9773vVx00UWVdffdd1/a2tpy0UUX5f3vf38N\nq6tPr+/1m5k9e3aVKuk/9Lw2TjvttPz3//7fa11Gv/Kb3/wml156aZ588slsscUW+dCHPpSLL764\n8qGX3qfn1afn1bdhw4Z0dXVlm222yQ9/+MO88MILOfTQQyv3LdYDYa4XmaSg+vS8+swwV316Xl3f\n/OY3s80222SvvfbaaPj8hz70oRpW1f/U24xzJdDz6tPzvvXUU0/lnnvuqeth84ZZ9qJvfvObWb58\neY477rgsX748F198sf/597HX9/z3v/99pk2bpud97Pnnn88DDzyQj33sY3nPe95TWb/11lvXsKr6\npufV9eCDDyZJ/tf/+l+VdQ0NDZX7dOl9bzXjnA+5fUfPq0/Pq+/MM8/MYYcdlu23377WpfQZV+Z6\nkUkKqk/Pq++www7LK6+8khdeeCENDQ3Zdttt09DQ4HlzfUjPNx/XXnttXf1Fd3Nx7LHH5utf/3qm\nTp2aa6+9NnfddVfe97735cgjj6x1aXVLz6tPz6vvb//2b3PjjTfWuow+5dEEveC+++7Lfffdlwce\neCAHHHBA3vOe92w0SQG9T89r58wzz8yGDRsyfPjwygQR5513Xo2rqm96vvkwO3Hf2HrrrfP+978/\nr776apqamnLCCSfkjjvuqHVZdU3Pq0/Pq+9v/uZvctZZZ+Xqq6/OtddeW/lXTwyz7AWvH4qTpDIJ\nyl/Wm3Gu9+l57XznO9/J97///QwZMiTJa0MAP/e5z2XChAk1rqx+6fnmw2CWvtEfZpzb3Oh59el5\n9V199dV1P8xSmOsFf5lRziQF1aPntbPDDjtkm222qSw3NTVl5513rmFF9U/PNx8efdI3Lr/88nR1\ndeXII4+szDh3/fXX17qsuqbn1afn1bfTTjtl8uTJtS6jTwlzvcgkBdWn59Vz+eWXp6GhIVtttVWO\nPvro7LXXXmloaMgvf/nLjBgxotbl1SU9p7945pln3jDj3IIFC9yf2If0vPr0vPp22WWXTJkyJWPG\njEljY2Nl/UknnVTDqnqXMNeL7rvvvvzkJz8xSUEV6Xn17L777knyhufhfOxjH6tFOf2Cnm9+DLPs\nG/1hxrnNjZ5Xn55XX1NTU5qamvLHP/6x1qX0GWGuF5155pmZN29ehg8fniRZu3atSQr6mJ5XzzHH\nHFPrEvodPa+Nz3zmM5kwYUL+y3/5L2lpadlo25w5c2pUVX3bcccd86UvfanWZfQrel59el59ixcv\nzr777pt99tknH//4xzd6dmi98GiCXvTpT386t9566xsmKfj+979f48rql54DvW3lypW5++6789Of\n/jTd3d057LDD8td//dcZNGhQrUurWz/+8Y/z/e9/P6NGjdpoKJThZ31Hz6tPz6uvo6MjDz/8cB5+\n+OH8+te/ztZbb5299torX/jCF2pdWq/xaIJeZJKC6tNzoLcNGzYsEydOzA033JBzzz03bW1tOfjg\ng3PRRRdl1apVtS6vLl199dX58Ic/nO23374yLKqpqanWZdU1Pa8+Pa++5ubmtLa25sADD6w8h/j/\n/J//U+Oqelf9XWusAZMUVJ+eA31lxYoV+dGPfpT//b//d3bYYYd8/vOfz9ixY/PQQw/l3HPPzT/9\n0z/VusS60x9mnNvc6Hn16Xn1TZgwIdtvv33Gjx+fsWPH5qyzzqq7oZb1dTY1YpKC6tNzoK9MmTIl\nn/70p3PjjTdWhnAnyb777pvW1tYaVla/+sOMc5sbPa8+Pa++M888M4888kjuv//+PPzwwxkzZkz2\n2GOPjBkzptal9RphrheYpKD69BzoK8OGDXvLD1eTJk2qcjX9Q3+YcW5zo+fVp+fVN2HChEyYMCHr\n1q1Le3t7br311lx55ZV5/PHHa11arxHmAOB1hgwZkrlz52bMmDEbPb/yoIMOqmFV9a0/zDi3udHz\n6tPz6ps5c2Z+85vfZIsttsg+++yTv/3bv811111X67J6ldksAeB1LrroojddP3v27CpX0n/0hxnn\nNjd6Xn16Xn0PPPD/2rufV9jiOIzjz1xGxqykZjGNpd1IlMnCVsJOWSDKDgs1Gwtqyo+NDYmklKaO\nwoaFUnJOVlOSTCh/AhvGLMRiNN2dztx7627M+c4c79dqmtXTs3s6p8/JaHFxUZJ0dnamtbU1dXZ2\nqru723Cy78OYAwDAZXt7W5OTk6Zj/Dhvb2+6vb1VNpvV3d2dPj4+ZFmW6Vi+Rufeo3NvjY6OanNz\nUzMzM7IsSy8vL5qentbh4aHpaN+G57sAALjkcjllMhm1traWvGYZCoUMpvK3n3BxrtLQuffo3Hu1\ntbVqbGxUIBCQJDU1NX399guezAEA4NLb26tCoVDyXyAQkOM4hhL538nJibLZrB4fHxUOh315ca7S\n0Ln36Nx78/PzikQism1bU1NTsm1b4XBYS0tLpqN9G8YcAAD/cXR0pMHBQdMxfM99ce76+tpXF+cq\nFZ17j869UywWv0Z0MBhUW1ub+vr6Sj4NUe0YcwAAuNzf32tnZ0f5fF6SVCgU9Pz8rPPzc8PJ/OvP\ni3OJRELt7e1qaGgwHc236Nx7dI5y+GU6AAAAlWR5eVkjIyN6f3/X7OysEomE5ubmTMfytZ6eHuXz\neeVyOSWTSV1dXenm5sZ0LF+jc+/ROcqBMQcAgEt9fb26urpUV1eneDyuZDKpvb0907F8bWtrSwcH\nB4pEIpKk8fFxbWxsGE7lb3TuPTpHOXBCBwAAl1AoJMdxFIvFtLq6qubmZj09PZmO5Ws/4eJcpaFz\n79E5yoExBwCASyqVkuM4SqVSSqfTWlhYUDqdNh3L12KxmNbX1/X6+qrT01PZtq2WlhbTsXyNzr1H\n5ygHDqAAAOAyMTGhoaEh9ff3S5IuLi5kWZZ2d3cNJ/Ovn3BxrtLQuffoHOXAmAMAwGV4eFj7+/sl\n/42NjcmyLEOJAAD4N16zBADAJRqNamVlRR0dHSoWi7q8vFQ0GjUdCwCAv/BkDgAAl8/PTx0fH+vh\n4UE1NTWKx+MaGBhQMBg0HQ0AgBKMOQAAAACoQnxnDgAAAACqEGMOAAAAAKoQYw4AAAAAqhBjDgAA\nAACqEGMOAAAAAKrQbz2jzMrj/j3JAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93fe94f908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_learn_results.mean(axis=1).plot.bar(yerr=p_learn_results.std(axis=1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2017-06-26T10:04:54.772Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f940d9460f0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f940d4f2c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_test_results.mean(axis=1).plot.bar()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
